gen_group_command.py¶
gen_group_command.py - generate group analysis command scripts
purpose:¶
Quickly generate group analysis command scripts by parsing wildcard-based
lists of input datasets.
1. generate group commands: 3dttest++, 3dMEMA, 3dANOVA2, 3dANOVA3
2. generate generic commands
This program is to assist in writing group commands. The hardest part (or
most tedious) is generally listing datasets and such, particularly including
sub-brick selection, and that is the main benefit of using this program.
If used without sufficient options (which might be typical), the generated
commands will not be complete (e.g. they might fail). So either provide
sufficient passed options via -options or plan to edit the resulting script.
If -write_script is not given, the command is written to stdout.
** NOTE: this program expects one dataset per subject. Single condition
volumes are accessed using sub-brick selectors via -subs_betas
and possibly -subs_tstats.
This program can parse subject IDs from dataset names when the IDs are the
varying part of dataset names (e.g. stats_subj1234+tlrc.HEAD), as in:
gen_group_command.py -command 3dttest++ \
-dsets stats*+tlrc.HEAD
or when the subject IDs are the varying part of the directory names (while
the actual file names are identical), as in:
gen_group_command.py -command 3dttest++ \
-dsets subject_results/*/*.results/stats+tlrc.HEAD
Generic commands do not need to be part of AFNI. Perhaps one just wants
an orderly and indented list of file names to be part of a bigger script.
consider:
gen_group_command.py -command ls -dsets group_results/OL*D
or perhaps using 3dTcat to collect a sub-brick from each subject:
gen_group_command.py -command 3dTcat -subs_betas 'Arel#0_Coef' \
-dsets group_results/OL*D
examples (by program)¶
A. 3dttest++ (not 3dttest)¶
Note: these commands apply to the sample group data under
AFNI_data6/group_results.
* Note: The 3dttest++ program defaults to setA minus setB, which is the
opposite of 3dttest and 3dMEMA (though it might be more natural).
The direction of the test can be further specified using either
-AminusB or -BminusA, which is always included in the resulting
command if there are 2 sets of data.
This program will always supply one of -AminusB or -BminusA, to be
clear. If the user does not provide one, -AminusB will be used.
Note also that 3dttest uses sub-brick labels which should make
this clear.
1. the most simple case, providing just the datasets¶
The most simple case, providing just the datasets. The subject IDs
will be extracted from the dataset names. Since no sub-bricks are
provided, the betas will default to sub-brick 0 and the test will be
the mean compared with 0.
gen_group_command.py -command 3dttest++ \
-dsets REML*.HEAD
2. specifying set labels and beta weights for a 2-sample t-test¶
Specify the sub-bricks and set labels to compare Vrel vs. Arel.
Write the command to the file cmd.tt++.2.
gen_group_command.py -command 3dttest++ \
-write_script cmd.tt++.2 \
-prefix tt++.2_V-A \
-dsets REML*.HEAD \
-set_labels Vrel Arel \
-subs_betas 'Vrel#0_Coef' 'Arel#0_Coef'
3. request a paired t-test and apply a mask¶
gen_group_command.py -command 3dttest++ \
-write_script cmd.tt++.3 \
-prefix tt++.3_V-A_paired \
-dsets REML*.HEAD \
-set_labels Vrel Arel \
-subs_betas 'Vrel#0_Coef' 'Arel#0_Coef' \
-options \
-paired -mask mask+tlrc
4. include options specific to 3dttest++ (not gen_group_command.py)¶
Exclude voxels that are identically zero across more than 20% of the
input datasets (presumably masked at the single subject level).
Convert output directly to z, since the DOF will vary across space.
gen_group_command.py -command 3dttest++ \
-write_script cmd.tt++.4 \
-prefix tt++.4_V-A_zskip \
-dsets REML*.HEAD \
-set_labels Vrel Arel \
-subs_betas 'Vrel#0_Coef' 'Arel#0_Coef' \
-options \
-zskip 0.8 -toz
6. specify index lists to restrict applied subject datasets¶
Use -dset_index0_list to compare female subjects to males.
Both subject types are in the same directory (10 subjects total).
So the -dsets options will both specify the same list, which will
then be paired down via -dset_index0_list to indicate only females
and only males.
Females have subject indices: 0, 1, 2, 3 and 5.
Males have subject indices: 4 and 6 through 9 (the last).
gen_group_command.py -command 3dttest++ \
-write_script cmd.tt++.6 \
-prefix tt++.6_F-M \
-dsets data/OLSQ*.HEAD \
-dset_index0_list '0..3,5' \
-dsets data/OLSQ*.HEAD \
-dset_index0_list '4,6..$' \
-set_labels female male \
-subs_betas 'Vrel#0_Coef'
7. specify applied subjects via subject ID lists¶
For BIDS, adjust subject IDs and get group lists from text files,
group1_subjects.txt and group2_subjects.txt.
gen_group_command.py \
-command 3dttest++ \
-write_script cmd.tt++.7 \
-prefix tt++.7_F-M \
-dsets sub-*/*.results/stats.sub*REML+tlrc.HEAD \
-dset_sid_list `cat group1_subjects.txt` \
-dsets sub-*/*.results/stats.sub*REML+tlrc.HEAD \
-dset_sid_list `cat group2_subjects.txt` \
-set_labels horses rabbits \
-subs_betas 'carrots#0_Coef'
See "3dttest++ -help" for details on its options.
B. 3dMEMA¶
Note: these commands apply to the sample group data under
AFNI_data6/group_results.
Note: As with 3dttest, group comparisons are done as the second set minus
the first set.
1. most simple case, providing only datasets¶
The most simple case, providing just the datasets. The subject IDs
will be extracted from the dataset names. Since no sub-bricks are
provided, the betas will be 0 and t-stats will be 1.
gen_group_command.py -command 3dMEMA \
-dsets REML*.HEAD
2. getting separate groups via directories¶
This does not quite apply to AFNI_data6. Assuming there are 2 group
directories, write a 2-sample command.
gen_group_command.py -command 3dMEMA \
-write_script cmd.mema.2 \
-dsets groupA/REML*.HEAD \
-dsets groupB/REML*.HEAD
3. restrict subject datasets via an index list¶
Run 3dMEMA, but restrict the subjects to partial lists from within
an entire list. This applies -dset_index0_list (or the sister
-dset_index1_list option).
# assume these 9 subjects represent all under the 'data' dir
set subjects = ( AA BB CC DD EE FF GG HH II )
a. Do a simple test on subjects AA, HH, II and FF. Indices are:
0-based: 0, 7, 8, 5 (AA=0, ..., II=8)
1-based: 1, 8, 9, 6 (AA=1, ..., II=9)
gen_group_command.py -command 3dMEMA \
-write_script cmd.mema.3a \
-dsets data/REML*.HEAD \
-dset_index0_list '0,7,8,5'
b. Do a test on sub-lists of subjects.
gen_group_command.py -command 3dMEMA \
-write_script cmd.mema.3b \
-dsets data/REML*.HEAD \
-dset_index0_list '0,7,8,5' \
-dsets data/REML*.HEAD \
-dset_index0_list '3,4,6,9' \
-subs_betas 'Arel#0_Coef' \
-subs_tstats 'Arel#0_Tstat'
See "3dMEMA -help" for details on the extra options.
C. 3dANOVA2¶
Note: these commands apply to the sample group data under
AFNI_data6/group_results.
Note: it seems better to create the script without any contrasts, and
add them afterwards (so the user can format well). However, if
no contrasts are given, the program will add 1 trivial one.
1. basic example, with datasets and volume indices¶
The most simple case, providing just the datasets and a list of
sub-bricks.
gen_group_command.py -command 3dANOVA2 \
-dsets OLSQ*.HEAD \
-subs_betas 0 1
2. get more useful:¶
- apply with a directory
- specify a script name
- specify a dataset prefix for the 3dANOVA2 command
- use labels for sub-brick indices
- specify a simple contrast
gen_group_command.py -command 3dANOVA2 \
-write_script cmd.A2.2 \
-prefix outset.A2.2 \
-dsets AFNI_data6/group_results/REML*.HEAD \
-subs_betas 'Vrel#0_Coef' 'Arel#0_Coef' \
-options \
-adiff 1 2 VvsA
D. 3dANOVA3¶
Note: these commands apply to the sample group data under
AFNI_data6/group_results.
Note: it seems better to create the script without any contrasts, and
add them afterwards (so the user can format well). However, if
no contrasts are given, the program will add 2 trivial ones,
just for a starting point.
Note: this applies either -type 4 or -type 5 from 3dANOVA3.
See "3dANOVA3 -help" for details on the types.
The user does not specify type 4 or 5.
type 4: there should be one -dsets option and a -factors option
type 5: there should be two -dsets options and no -factor
1. 3dANOVA3 -type 4 : simple¶
This is a simple example of a 2-way factorial ANOVA (color by image
type), across many subjects. The colors are pink and blue, while the
images are of houses, faces and donuts. So there are 6 stimulus types
in this 2 x 3 design:
pink house pink face pink donut
blue house blue face blue donut
Since those were the labels given to 3dDeconvolve, the beta weights
will have #0_Coef appended, as in pink_house#0_Coef. Note that in a
script, the '#' character will need to be quoted.
There is only one set of -dsets given, as there are no groups.
gen_group_command.py -command 3dANOVA3 \
-dsets OLSQ*.HEAD \
-subs_betas \
"pink_house#0_Coef" "pink_face#0_Coef" "pink_donut#0_Coef" \
"blue_house#0_Coef" "blue_face#0_Coef" "blue_donut#0_Coef" \
-factors 2 3
2. 3dANOVA3 -type 4 : more useful¶
Get more useful:
- apply with an input data directory
- specify a script name
- specify a dataset prefix for the 3dANOVA3 command
- specify simple contrasts
gen_group_command.py -command 3dANOVA3 \
-write_script cmd.A3.2 \
-prefix outset.A3.2 \
-dsets AFNI_data6/group_results/OLSQ*.HEAD \
-subs_betas \
"pink_house#0_Coef" "pink_face#0_Coef" "pink_donut#0_Coef" \
"blue_house#0_Coef" "blue_face#0_Coef" "blue_donut#0_Coef" \
-factors 2 3 \
-options \
-adiff 1 2 pink_vs_blue \
-bcontr -0.5 -0.5 1.0 donut_vs_house_face
3. 3dANOVA3 -type 5 : simple, with 2 groups¶
Here is a simple case, providing just 2 groups of datasets and a list
of sub-bricks.
gen_group_command.py -command 3dANOVA3 \
-dsets OLSQ*.HEAD \
-dsets REML*.HEAD \
-subs_betas 0 1
4. 3dANOVA3 -type 5 : more detailed¶
Get more useful:
- apply with an input data directory
- specify a script name
- specify a dataset prefix for the 3dANOVA3 command
- use labels for sub-brick indices
- specify simple contrasts
gen_group_command.py -command 3dANOVA3 \
-write_script cmd.A3.4 \
-prefix outset.A3.2 \
-dsets AFNI_data6/group_results/OLSQ*.HEAD \
-dsets AFNI_data6/group_results/REML*.HEAD \
-subs_betas 'Vrel#0_Coef' 'Arel#0_Coef' \
-options \
-adiff 1 2 OvsR \
-bdiff 1 2 VvsA
E. generic/other programs¶
These commands apply to basically any program, as specified. Options
may be provided, along with 1 or 2 sets of data. If provided, the
-subs_betas selectors will be applied.
This might be useful for simply making part of a longer script, where
the dataset names are explicit.
1. very simple demonstration, for just an ‘ls’ command¶
Perhaps a fairly useless example with 'ls', just for demonstration.
gen_group_command.py -command ls -dsets group_results/OL*D
2. using 3dTcat to collect a sub-brick from each subject¶
gen_group_command.py -command 3dTcat -subs_betas 'Arel#0_Coef' \
-dsets group_results/OL*D
3. including 2 sets of subjects, with a different sub-brick per set¶
gen_group_command.py -command 3dTcat -subs_betas 0 1 \
-dsets group_results/OLSQ*D \
-dsets group_results/REML*D
4. 2 sets of subjects¶
Datasets in different directories, and with different sub-brick
selectors, along with:
- a script name (to write the script to a text file)
- a -prefix
- options for the command (just 1 in this case)
- common sub-brick selectors for dataset lists
gen_group_command.py -command 3dMean \
-write_script cmd.3dmean.txt \
-prefix aud_vid_stdev \
-options -stdev \
-subs_betas 'Arel#0_Coef' \
-dsets group_results/OLSQ*D \
-dsets group_results/REML*D
F. datatable creation¶
These are examples of how to create a datatable file, suitable for
input via -dataTable to 3dMVM, 3dLME, etc.
apply via: -command datatable
Note: at this time, the output is specific to an external datatable file,
rather than one to paste on the command line (the difference being
quotes for sub-brick selectors and line continuation characters,
i.e. \ at the end of a line).
The purpose of this option is to specify datasets and possibly volume
labels (sub-brick selectors) and a set of task attributes that would
connect each subject volume (beta weight) to one attribute set. This
is based on a full factorization of the attributes. Each attribute gets
a column in the output datatable.
Optionally, one can also specify across subject attribute, one set per
subject. Such columns are then duplicated for each row of a given
subject.
* Note that missing volumes are allowed by this program, but only when the
input volumes for a single subject are in separate files, as with
example 2.
Creation of a datatable is divided into logical components:
A. a table of subject attributes that is not paired to datasets,
but has one fixed entry per subject
e.g. -dt_tsv my_glorious_attributes.txt
my_glorious_attributes.txt :
Subj Group Score Age
subj-0044 A -33.33 24
subj-0060 B 36.84 19
B. the actual dataset inputs: 2 ways to do it
(either way, per subject)
i. one data file per factor level (task attribute)
- so each data set will have a single volume
e.g. -dsets results/sub*/cond.A/sub*.nii.gz \
-dsets results/sub*/cond.B/sub*.nii.gz \
-dsets results/sub*/cond.C/sub*.nii.gz \
-dt_factor_list ... \
ii. one set of input and factor-corresponding sub-brick selectors
(either way, factors are listed for dset volume correspondence)
e.g. -dsets results/sub*/cond.A.B.C/sub*.nii.gz \
-dt_factor_list ... ... ... \
-subs_betas B_R_T1 B_R_T2 B_R_T3 ... \
Correspondence between TSV, input datasets, factors and betas:¶
- Subject IDs must be extractable from the input dataset names (i.e.
the program should be able to guess them from the part of the input
files that varies across the names). This applies to any use of
gen_group_command.py, not just for datatable.
IDs starting with sub/subj are more readily found in their entirety.
Such found IDs must match Subj entries in any -dt_tsv file.
- The -factor list options should define volumes in a factor-major
order, say. So the first factor list is the slowest changing, down
to the last factor list being the fastest changing. These are like
digits of sequential integers, where the first factors are the
left-most "digit" position, and the last factors are the right-most.
The first parameter of -dt_factor_list is the column label, and the
rest are the actual factor levels or values.
Consider the factor lists from example 1 (2 x 2 x 3 factors):
-dt_factor_list visit before after \
-dt_factor_list color red green \
-dt_factor_list task T1 T2 T3 \
Here 'visit' has 2 levels, 'color' has 2 and 'task' has 3. So there
are 12 = 2x2x3 combinations in this factorization.
The order of these factor sets mapping to dataset volumes (i.e. the
order of the -subs_betas arguments or the order of the -dsets
options) as specified is, first to last:
most sig next most sig least significant
-------- ------------- -----------------
before red T1
before red T2
before red T3
before green T1
before green T2
before green T3
after red T1
after red T2
after red T3
after green T1
after green T2
after green T3
- If there is only one -dsets line (so each subject dataset contains
all input volumes), then there should be a -subs_betas option given.
In this case, the order of the factor combinations should match the
order of the -subs_betas arguments.
If there is more than one -dsets line, there must be exactly as many
-dsets lines as there are are factor combinations, 12 in example 1.
Here, the first -dsets option would correspond to before-red-T1, and
the last/12th -dsets option would correspond to after-green-T3.
Where were we? Oh right, examples...
1. simple: no -dt_tsv, one -dsets option, with -subs_betas¶
This skips part A above, generating basically an ANOVA table without
subject-specific attributes.
Only one -dsets option implies one dataset per subject, so all factor
levels/sub-bricks/task attrs exist in each subject dataset. This
requires -subs_betas to connect task attrs to sub-bricks, listing the
sub-bricks that correspond with the ordered combination of factors.
Note that betas should be in factor-major order, where the first
factor changes the slowest (so here all 'before' betas come before all
'after' betas, and then with reds before greens, etc).
gen_group_command.py \
-command datatable \
-dsets all_results/sub*.nii.gz \
-dt_factor_list visit before after \
-dt_factor_list color red green \
-dt_factor_list task T1 T2 T3 \
-subs_betas B_R_T1 B_R_T2 B_R_T3 \
B_G_T1 B_G_T2 B_G_T3 \
A_R_T1 A_R_T2 A_R_T3 \
A_G_T1 A_G_T2 A_G_T3
* to restrict to a specific list of subjects, include something like:
-dset_sid_list $my_favorite_subjects
2. simple: no -dt_tsv, one -dsets option per factor combination¶
Like 1, but with each subject beta volume in a separate dataset
(so no -subs_betas option is applied). The generated table should be
similar to that from 1, with identical ordering, but using varying
files rather than beta volume indexing.
gen_group_command.py \
-command datatable \
-dt_factor_list visit before after \
-dt_factor_list color red green \
-dt_factor_list task T1 T2 T3 \
-dsets all_results/data.B_R_T1/sub*.gz \
-dsets all_results/data.B_R_T2/sub*.gz \
-dsets all_results/data.B_R_T3/sub*.gz \
-dsets all_results/data.B_G_T1/sub*.gz \
-dsets all_results/data.B_G_T2/sub*.gz \
-dsets all_results/data.B_G_T3/sub*.gz \
-dsets all_results/data.A_R_T1/sub*.gz \
-dsets all_results/data.A_R_T2/sub*.gz \
-dsets all_results/data.A_R_T3/sub*.gz \
-dsets all_results/data.A_G_T1/sub*.gz \
-dsets all_results/data.A_G_T2/sub*.gz \
-dsets all_results/data.A_G_T3/sub*.gz
3. include -dt_tsv, with one -dsets option per factor combination¶
The -dt_tsv option can be a simple addition to either of the above
examples. Each subject would then have their row of the TSV included
in each of their output rows. Here we pass subject_attrs.tsv.
Same as 2, but include:
-dt_tsv subject_attrs.tsv
gen_group_command.py \
-command datatable \
-dt_tsv subject_attrs.tsv \
-dt_factor_list visit before after \
-dt_factor_list color red green \
-dt_factor_list task T1 T2 T3 \
-dsets all_results/data.B_R_T1/sub*.gz \
-dsets all_results/data.B_R_T2/sub*.gz \
-dsets all_results/data.B_R_T3/sub*.gz \
-dsets all_results/data.B_G_T1/sub*.gz \
-dsets all_results/data.B_G_T2/sub*.gz \
-dsets all_results/data.B_G_T3/sub*.gz \
-dsets all_results/data.A_R_T1/sub*.gz \
-dsets all_results/data.A_R_T2/sub*.gz \
-dsets all_results/data.A_R_T3/sub*.gz \
-dsets all_results/data.A_G_T1/sub*.gz \
-dsets all_results/data.A_G_T2/sub*.gz \
-dsets all_results/data.A_G_T3/sub*.gz
test. test examples F1, F2 and F3 by abusing the shell¶
If one wanted to be sneaky and test these examples with a set of
10 random subject names and corresponding empty files, then before
running 1 or 2, consider (here in 'tcsh' syntax):
# make lists of beta labels and subject codes
set bstr = '{B,A}_{R,G}_T{1,2,3}'
set sstr = '{0044,0046,0049,0053,0060,0061,0064,0073,0075,0076}'
# create a directory tree for example F1, and then run F1
mkdir all_results
touch all_results/sub-$sstr.nii.gz
# run command F1 here
# create a directory tree for example F2, and then run F2
mkdir -p all_results/data.$bstr
touch all_results/data.$bstr/sub-$sstr.nii.gz
# run command F2 here
# create an additional attributes file, and then run F3
echo Subj Group ValA ValB > subject_attrs.tsv
foreach subj ( $sstr )
echo sub-$subj G_$subj VA_$subj VB_$subj >> subject_attrs.tsv
end
# run command F3 here
command-line options:¶
terminal options:¶
-help : show this help
-hist : show module history
-show_valid_opts : list valid options
-ver : show current version
required parameters:¶
-command COMMAND_NAME : resulting command, such as 3dttest++¶
The current list of group commands is: 3dttest++, 3dMEMA, 3dANOVA2,
3dANOVA3.
3dANOVA2 : applied as -type 3 only (factor x subjects)
3dANOVA3 : -type 4: condition x condition x subject
(see -factors option)
-type 5: group x condition x subject
3dMEMA : pairing betas and t-stats
3dttest++ : allows basically full control
datatable : generate -dataTable files for Gang's R stats programs
-dsets datasets … : list of input datasets¶
Each use of this option essentially describes one group of subjects.
All volumes for a given subject should be in a single dataset.
This option can be used multiple times, once per group.
other options:¶
-dset_sid_list SID SID … : restrict -dsets datasets to this SID list¶
In some cases it is easy to use a wildcard to specify all datasets via
-dsets, but where subject groups would not be partitioned that way.
For example, you have a list of subjects to apply, per group, but no
way to separate them with a wildcard (e.g. in a BIDS tree, with no
group directories).
Consider this example:
-subj_prefix sub- \
-dsets sub-*/*.results/stats.sub*REML+tlrc.HEAD \
-dset_sid_list sub-0* \
or make 2 subject lists, each starting with all subjects, but with
group lists contained in text files:
-subj_prefix sub- \
-dsets sub-*/*.results/stats.sub*REML+tlrc.HEAD \
-dset_sid_list `cat group1_subjects.txt` \
-dsets sub-*/*.results/stats.sub*REML+tlrc.HEAD \
-dset_sid_list `cat group2_subjects.txt` \
This option specifies subjects to include, while -dset_sid_omit_list
specifies subjects to exclude.
-dset_sid_omit_list SID SID … : omit these SIDs from -dsets datasets¶
In some cases it is easy to use a wildcard to specify all datasets via
-dsets, and then to remove select subjects IDs found from that wildcard
list. For example, this can be used to omit subjects dropped due to
quality control considerations.
One -dset_sid_omit_list option should be provided per -dsets option,
(to omit subjects from that particular list), or else one
-dset_sid_omit_list option should be provided to apply to all -dsets
options.
The SID entries must match the subject IDs found from -dsets.
Consider this example:
-dsets sub-*/*.results/stats.sub*REML+tlrc.HEAD \
-dset_sid_omit_list sub-010 sub-117 sub-358 \
Here all subject IDs found from the initial wildcard would initially be
included, but then sub-010, sub-117 and sub-358 would be removed from
that list.
This option specifies subjects to exclude, while -dset_sid_list
specifies subjects to include.
-dset_index0_list values… : restrict -dsets datasets to a 0-based list¶
-dset_index1_list values… : restrict -dsets datasets to a 1-based list¶
In some cases it is easy to use a wildcard to specify datasets via
-dsets, but there may be a grouping of subjects within that list.
For example, if both males and females are in the list of datasets
provided by -dsets, and if one wants a comparison between those 2
groups, then a pair of -dset_index0_list could be specified (1 for
each -dset) option to list which are the females and males.
Consider this example:
-dsets all/stats.*.HEAD \
-dset_index0_list '0..5,10..15' \
-dsets all/stats.*.HEAD \
-dset_index0_list '6..9,16..$' \
Note that -dsets is used twice, with IDENTICAL lists of datasets.
The respective -dset_index0_list options then restrict those lists to
0-based index lists, one for females, the other for males.
* One must be careful to get the indices correct, so check the output
command script to be sure the correct subjects are in each group.
The difference between -dset_index0_list and -dset_index1_list is just
that the former is a 0-based list (such as is used by AFNI programs),
while the latter is 1-based (such as is used by tcsh). A 0-based list
begins counting at 0 (as in offsets), while a list 1-based starts at 1.
Since use of either makes sense, both are provided.
For example, these options are equivalent:
-dset_index0_list 0,5..8
-dset_index1_list 1,6..9
The format for these index lists is the same as for AFNI sub-brick
selection.
-dt_factor_list LABEL V1 V2 … : specify a factor label and value list¶
example: -dt_factor_list Visit before after
-dt_factor_list Food pizza carrot chocolate
-dt_factor_list Task T1 T2 T3
for: -command datatable
Use this option to specify a factor label (the datatable column header
for that factor type) and a set of factor levels/values for it.
The full factorization of all such options would define the number of
volumes/sub-bricks to be input for each subject (ignoring missing
data).
For example, using just:
-dt_factor_list Task T1 T2 T3
each subject would have 3 volumes/beta weights of input, one for each
task type T1, T2 and T3.
But if 3 just options were used, as in:
-dt_factor_list Visit before after
-dt_factor_list Food pizza carrot chocolate
-dt_factor_list Task T1 T2 T3
Then each subject would have 18 (= 2*3*3) volumes of input:
before-pizza-T1
before-pizza-T2
before-pizza-T3
after-chocolate-T3
To see the full list, consider running the shell command:
echo {before,after}-{pizza,carrot,chocolate}-{T1,T2,T3}
or extending it with:
echo {before,after}-{pizza,carrot,chocolate}-{T1,T2,T3} \
tr ' ' '\n'
Each of these factor combinations would then refer to a single volume
of data for each subject.
These 18 volumes per subject would input using either:
18 -dsets options, each listing all subject volumes for that beta
or, if all 18 volumes are in a single subject dataset:
1 -dsets option, listing all subject datasets
1 -subs_betas option, listing all sub-brick selectors
(as integers or as labels, such as those from the 'echo' commands)
-dt_sep SEP : specify separator between table columns¶
example: -dt_sep '\t'
default: -dt_sep ' '
for: -command datatable
The default separation between the output datatable columns is varying
space, so the columns are visually aligned using a minimum of 2 spaces.
Use this option to modify the separator, such as using tabs, '\t'.
-dt_tsv TSV_FILE : specify a subject parameter file¶
example: -dt_tsv subject_attrs.tsv
for: -command datatable
The output data table would have a Subj column, factor/attribute
columns (from -dt_factor_list options) and an Inputfile column. Use
this option to provide a TSV file with a Subj column and columns for
any desired subject-specific attributes (group, age, ave reaction time,
etc).
For each subject in the output datatable, the -dt_tsv attribute columns
will also be included. Note that the Subj ID must match between this
TSV file and what is parsed from the input -dsets lists.
-factors NF1 NF2 … : list of factor levels, per condition¶
example: -factors 2 3
This option is currently only for '3dANOVA3 -type 4', which is a
condition x condition x subject test. It is meant to parse the
-subs_betas option, which lists all sub-bricks input to the ANOVA.
Assuming condition A has nA levels, and B has nB (2 and 3 in the
above example), then this option (applied '-factors nA nB', and
-subs_betas) would take nA * nB parameters (for the cross product of
factor A and factor B levels).
The betas should be specified in A major order, as in:
-subs_betas A1B1_name A1B2_name ... A1BnB A2B1 A2B2 ... AnABnB_name
or as in the 2 x 3 case:
-subs_betas A1B1 A1B2 A1B3 A2B1 A2B2 A2B3 -factors 2 3
e.g. for pink/blue x house/face/donut, output be 3dDeconvolve
(i.e. each betas probably has #0_Coef attached)
-subs_betas \
"pink_house#0_Coef" "pink_face#0_Coef" "pink_donut#0_Coef" \
"blue_house#0_Coef" "blue_face#0_Coef" "blue_donut#0_Coef" \
-factors 2 3 \
Again, these factor combination names should be either sub-brick labels
or indices (labels are suggested, to avoid confusion).
See the example with '3dANOVA3 -type 4' as part of example D, above.
See also -subs_betas.
-keep_dirent_pre : keep directory entry prefix¶
Akin to -subj_prefix, this flag expands the subject prefix list to
include everything up to the beginning of the directory names (at
the level that varies across input datasets).
By default, if names start with 'sub', this will be applied.
Example 1:
datasets:
subj.FP/betas+tlrc subj.FR/betas+tlrc subj.FT/betas+tlrc
subj.FV/betas+tlrc subj.FW/betas+tlrc subj.FX/betas+tlrc
subj.FY/betas+tlrc subj.FZ/betas+tlrc
The default subject IDs would be:
P R T V W X Y Z
When using -keep_dirent_pre, subject IDs would be:
subj.FP subj.FR subj.FT subj.FV subj.FW subj.FX subj.FY subj.FZ
Note that these IDs come at the directory level, since the dataset
names do not vary.
Example 2:
datasets:
subj.FP/OLSQ.FP.betas+tlrc subj.FR/OLSQ.FR.betas+tlrc
subj.FT/OLSQ.FT.betas+tlrc subj.FV/OLSQ.FV.betas+tlrc
subj.FW/OLSQ.FW.betas+tlrc subj.FX/OLSQ.FX.betas+tlrc
subj.FY/OLSQ.FY.betas+tlrc subj.FZ/OLSQ.FZ.betas+tlrc
The default subject IDs would be:
P R T V W X Y Z
When using -keep_dirent_pre, subject IDs would be:
OLSQ.FP OLSQ.FR OLSQ.FT OLSQ.FV OLSQ.FW OLSQ.FX OLSQ.FY OLSQ.FZ
Note that these IDs come at the dataset level, since the dataset
names vary.
-hpad PAD : pad subject prefix by PAD chars left¶
Akin to -subj_prefix and -tpad, this flag expands the subject prefix
list to include PAD extra characters toward the head/beginning.
See also -tpad.
-tpad PAD : pad subject prefix by PAD chars right¶
Akin to -subj_prefix and -hpad, this flag expands the subject prefix
list to include PAD extra characters toward the tail/end.
See also -hpad.
-options OPT1 OPT2 … : list of options to pass along to result¶
The given options will be passed directly to the resulting command. If
the -command is 3dMEMA, say, these should be 3dMEMA options. This
program will not evaluate or inspect the options, but will put them at
the end of the command.
-prefix PREFIX : apply as COMMAND -prefix¶
-set_labels LAB1 LAB2 … : labels corresponding to -dsets entries¶
-subj_prefix PREFIX : prefix for subject names (3dMEMA)¶
-subj_suffix SUFFIX : suffix for subject names (3dMEMA)¶
-subs_betas B0 B1 : sub-bricks for beta weights (or similar)¶
If this option is not given, sub-brick 0 will be used. The entries
can be either numbers or labels (which should match what is seen in
the afni GUI, for example).
If there are 2 -set_labels, there should be 2 betas (or no option).
-subs_tstats T0 T1 : sub-bricks for t-stats (3dMEMA)¶
If this option is not given, sub-brick 1 will be used. The entries can
be either numbers or labels (which should match what is seen in the
afni GUI, for example).
This option applies only to 3dMEMA currently, and in that case, its use
should match that of -subs_betas.
See also -subs_betas.
-type TEST_TYPE : specify the type of test to perform¶
The test type may depend on the given command, but generally implies
there are multiple sets of values to compare. Currently valid tests
are (for the given program):
3dMEMA: paired, unpaired
If this option is not applied, a useful default will be chosen.
-verb LEVEL : set the verbosity level¶
-write_script FILE_NAME : write command script to FILE_NAME¶
If this option is given, the command will be written to the specified
file name. Otherwise, it will be written to the terminal window.
R Reynolds October 2010
